The Penobscot River Restoration Project (PRRP), to be completed in 2016, involved an extensive plan of dam removal, increases in hydroelectric capacity, and fish passage modifications to increase habitat access for diadromous species. As part of the PRRP, Great Works and Veazie dams were removed, making Milford Dam the first impediment to federally endangered Atlantic Salmon Salmo salar. Upstream habitat access for Atlantic Salmon is dependent upon successful and timely passage at Milford Dam because nearly all suitable spawning habitat is located upstream. In 2014 and 2015, a total of 73 adult salmon were radio-tagged to track their upstream movements through the Penobscot River to assess potential delays at (1) the dam remnants, (2) the confluence of the Stillwater Branch and the main stem of the Penobscot River below the impassable Orono Dam, and (3) the Milford Dam fish lift (installed in 2014). Movement rates through the dam remnants and the Stillwater confluence were comparable to open river reaches. Passage efficiency of the fish lift was high in both years (95% and 100%). However, fish experienced long delays at Milford Dam, with approximately one-third of fish taking more than a week to pass in each year, well below the Federal Energy Regulatory Commission passage standard of 95% within 48 h. Telemetry indicates most fish locate the fishway entrance within 5 h of arrival and were observed at the entrance at all hours of the day. These data indicate that overall transit times through the lower river were comparable to reported movement rates prior to changes to the Penobscot River due to the substantial delays seen at Milford Dam. The results of this study show that while adult Atlantic Salmon locate the new fish lift entrance quickly, passage of these fish was significantly delayed under 2014–2015 operations.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/19425120.2016.1185063","usgsCitation":"Izzo, L.K., Maynard, G.A., and Zydlewski, J.D., 2016, Upstream movements of Atlantic Salmon in the Lower Penobscot River, Maine following two dam removals and fish passage modifications: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 8, no. 1, p. 448-461, https://doi.org/10.1080/19425120.2016.1185063.","productDescription":"14 p.","startPage":"448","endPage":"461","ipdsId":"IP-072944","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470381,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/19425120.2016.1185063","text":"Publisher Index Page"},{"id":337493,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Penobscot River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.79981994628906,\n 44.76038647589176\n ],\n [\n -68.62266540527344,\n 44.76038647589176\n ],\n [\n -68.62266540527344,\n 44.990055522906864\n ],\n [\n -68.79981994628906,\n 44.990055522906864\n ],\n [\n -68.79981994628906,\n 44.76038647589176\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-21","publicationStatus":"PW","scienceBaseUri":"58c90124e4b0849ce97abcc3","contributors":{"authors":[{"text":"Izzo, Lisa K.","contributorId":189241,"corporation":false,"usgs":false,"family":"Izzo","given":"Lisa","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":684200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maynard, George A.","contributorId":189242,"corporation":false,"usgs":false,"family":"Maynard","given":"George","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":684201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":684034,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70185762,"text":"70185762 - 2016 - Hurricane disturbance benefits nesting American Oystercatchers (Haematopus palliatus)","interactions":[],"lastModifiedDate":"2017-03-29T10:28:39","indexId":"70185762","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"title":"Hurricane disturbance benefits nesting American Oystercatchers (Haematopus palliatus)","docAbstract":"Coastal ecosystems are under increasing pressure from human activity, introduced species, sea level rise, and storm activity. Hurricanes are a powerful destructive force, but can also renew coastal habitats. In 2003, Hurricane Isabel altered the barrier islands of North Carolina, flattening dunes and creating sand flats. American Oystercatchers (Haematopus palliatus) are large shorebirds that inhabit the coastal zone throughout the year. Alternative survival models were evaluated for 699 American Oystercatcher nests on North Core Banks and South Core Banks, North Carolina, USA, from 1999–2007. Nest survival on North Core Banks increased from 0.170 (SE = 0.002) to 0.772 (SE = 0.090) after the hurricane, with a carry-over effect lasting 2 years. A simple year effects model described nest survival on South Core Banks. Habitat had no effect on survival except when the overall rate of nest survival was at intermediate levels (0.300–0.600), when nests on open flats survived at a higher rate (0.600; SE = 0.112) than nests in dune habitat (0.243; SE = 0.094). Predator activity declined on North Core Banks after the hurricane and corresponded with an increase in nest survival. Periodic years with elevated nest survival may offset low annual productivity and contribute to the stability of American Oystercatcher populations.
","language":"English","publisher":"The Waterbird Society","doi":"10.1675/063.039.0402","usgsCitation":"Simons, T.R., and Schulte, S., 2016, Hurricane disturbance benefits nesting American Oystercatchers (Haematopus palliatus): Waterbirds, v. 39, no. 4, p. 327-337, https://doi.org/10.1675/063.039.0402.","productDescription":"11 p.","startPage":"327","endPage":"337","ipdsId":"IP-057574","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":338548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58dcc7d5e4b02ff32c685673","contributors":{"authors":[{"text":"Simons, Theodore R. 0000-0002-1884-6229 tsimons@usgs.gov","orcid":"https://orcid.org/0000-0002-1884-6229","contributorId":2623,"corporation":false,"usgs":true,"family":"Simons","given":"Theodore","email":"tsimons@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":686695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schulte, Shiloh A.","contributorId":39911,"corporation":false,"usgs":true,"family":"Schulte","given":"Shiloh A.","affiliations":[],"preferred":false,"id":686762,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70185045,"text":"70185045 - 2016 - Increasing aeolian dust deposition to snowpacks in the Rocky Mountains inferred from snowpack, wet deposition, and aerosol chemistry","interactions":[],"lastModifiedDate":"2017-03-13T17:07:57","indexId":"70185045","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":924,"text":"Atmospheric Environment","active":true,"publicationSubtype":{"id":10}},"title":"Increasing aeolian dust deposition to snowpacks in the Rocky Mountains inferred from snowpack, wet deposition, and aerosol chemistry","docAbstract":"Mountain snowpacks are a vital natural resource for ∼1.5 billion people in the northern Hemisphere, helping to meet human and ecological demand for water in excess of that provided by summer rain. Springtime warming and aeolian dust deposition accelerate snowmelt, increasing the risk of water shortages during late summer, when demand is greatest. While climate networks provide data that can be used to evaluate the effect of warming on snowpack resources, there are no established regional networks for monitoring aeolian dust deposition to snow. In this study, we test the hypothesis that chemistry of snow, wet deposition, and aerosols can be used as a surrogate for dust deposition to snow. We then analyze spatial patterns and temporal trends in inferred springtime dust deposition to snow across the Rocky Mountains, USA, for 1993–2014. Geochemical evidence, including strong correlations (r2 ≥ 0.94) between Ca2+, alkalinity, and dust concentrations in snow deposited during dust events, indicate that carbonate minerals in dust impart a strong chemical signature that can be used to track dust deposition to snow. Spatial patterns in chemistry of snow, wet deposition, and aerosols indicate that dust deposition increases from north to south in the Rocky Mountains, and temporal trends indicate that winter/spring dust deposition increased by 81% in the southern Rockies during 1993–2014. Using a multivariate modeling approach, we determined that increases in dust deposition and decreases in springtime snowfall combined to accelerate snowmelt timing in the southern Rockies by approximately 7–18 days between 1993 and 2014. Previous studies have shown that aeolian dust emissions may have doubled globally during the 20th century, possibly due to drought and land-use change. Climate projections for increased aridity in the southwestern U.S., northern Africa, and other mid-latitude regions of the northern Hemisphere suggest that aeolian dust emissions may continue to increase, compounding the risk that climate warming poses to snowpack water resources in arid/semi-arid regions of the world.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.atmosenv.2016.06.076","usgsCitation":"Clow, D.W., Williams, M.W., and Schuster, P.F., 2016, Increasing aeolian dust deposition to snowpacks in the Rocky Mountains inferred from snowpack, wet deposition, and aerosol chemistry: Atmospheric Environment, v. 146, p. 183-194, https://doi.org/10.1016/j.atmosenv.2016.06.076.","productDescription":"12 p.","startPage":"183","endPage":"194","ipdsId":"IP-073260","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":470368,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.atmosenv.2016.06.076","text":"Publisher Index Page"},{"id":337479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"146","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58c7af9ce4b0849ce9795e7a","contributors":{"authors":[{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":684061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Mark W.","contributorId":43046,"corporation":false,"usgs":true,"family":"Williams","given":"Mark","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":684062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schuster, Paul F. 0000-0002-8314-1372 pschuste@usgs.gov","orcid":"https://orcid.org/0000-0002-8314-1372","contributorId":1360,"corporation":false,"usgs":true,"family":"Schuster","given":"Paul","email":"pschuste@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":684063,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70175624,"text":"70175624 - 2016 - Use of repeat surveys and flow and sediment transport modeling to support fish spawning reef placement in the Detroit River, MI","interactions":[],"lastModifiedDate":"2017-04-19T13:48:02","indexId":"70175624","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Use of repeat surveys and flow and sediment transport modeling to support fish spawning reef placement in the Detroit River, MI","docAbstract":"The introduction of rock-rubble substrate in rivers, to enhance fish spawning habitat, represents a significant investment in planning, permitting and construction. Where river processes deposit sediment and cover the substrate, its value as fish spawning habitat can be diminished. Therefore, it is crucial in the site se-lection process, that substrate be placed in locations that benefit the fish species of concern and, to the extent possible, ensure its longevity and ecological function. Changes in river bed elevation in these locations due to infilling by bedload sediment can be determined through serial hydrographic surveys. However, in cases where this information is sparse or unavailable, flow and sediment transport modeling can be used to calcu-late sediment mobility in the rock placement locations. In this paper we will demonstrate how these tech-niques are applied in the process of planning for the placement of rock-rubble substrate (reefs) in the Detroit River, Michigan.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of River Flow 2016","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"River Flow 2016","conferenceDate":"Junly 10-14, 2016","conferenceLocation":"St. Louis, MO","language":"English","publisher":"CRC","doi":"10.1201/9781315644479-319","usgsCitation":"Kinzel, P.J., Nelson, J.M., Kennedy, G.W., and Bennion, D., 2016, Use of repeat surveys and flow and sediment transport modeling to support fish spawning reef placement in the Detroit River, MI, in Proceedings of River Flow 2016, St. Louis, MO, Junly 10-14, 2016, p. 2047-2054, https://doi.org/10.1201/9781315644479-319.","productDescription":"8 p.","startPage":"2047","endPage":"2054","ipdsId":"IP-073036","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":339978,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-06","publicationStatus":"PW","scienceBaseUri":"58f877b8e4b0b7ea54521c16","contributors":{"authors":[{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":645861,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, Jonathan M. 0000-0002-7632-8526 jmn@usgs.gov","orcid":"https://orcid.org/0000-0002-7632-8526","contributorId":2812,"corporation":false,"usgs":true,"family":"Nelson","given":"Jonathan","email":"jmn@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":645862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kennedy, Gregory W. 0000-0003-1686-6960 gkennedy@usgs.gov","orcid":"https://orcid.org/0000-0003-1686-6960","contributorId":3700,"corporation":false,"usgs":true,"family":"Kennedy","given":"Gregory","email":"gkennedy@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":645863,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bennion, David 0000-0003-4927-4195 dbennion@usgs.gov","orcid":"https://orcid.org/0000-0003-4927-4195","contributorId":149533,"corporation":false,"usgs":true,"family":"Bennion","given":"David","email":"dbennion@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":645864,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193660,"text":"70193660 - 2016 - Structured decision making as a framework for large-scale wildlife harvest management decisions","interactions":[],"lastModifiedDate":"2017-11-05T21:40:27","indexId":"70193660","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Structured decision making as a framework for large-scale wildlife harvest management decisions","docAbstract":"Fish and wildlife harvest management at large spatial scales often involves making complex decisions with multiple objectives and difficult tradeoffs, population demographics that vary spatially, competing stakeholder values, and uncertainties that might affect management decisions. Structured decision making (SDM) provides a formal decision analytic framework for evaluating difficult decisions by breaking decisions into component parts and separating the values of stakeholders from the scientific evaluation of management actions and uncertainty. The result is a rigorous, transparent, and values-driven process. This decision-aiding process provides the decision maker with a more complete understanding of the problem and the effects of potential management actions on stakeholder values, as well as how key uncertainties can affect the decision. We use a case study to illustrate how SDM can be used as a decision-aiding tool for management decision making at large scales. We evaluated alternative white-tailed deer (Odocoileus virginianus) buck-harvest regulations in New York designed to reduce harvest of yearling bucks, taking into consideration the values of the state wildlife agency responsible for managing deer, as well as deer hunters. We incorporated tradeoffs about social, ecological, and economic management concerns throughout the state. Based on the outcomes of predictive models, expert elicitation, and hunter surveys, the SDM process identified management alternatives that optimized competing objectives. The SDM process provided biologists and managers insight about aspects of the buck-harvest decision that helped them adopt a management strategy most compatible with diverse hunter values and management concerns.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.1613","usgsCitation":"Robinson, K., Fuller, A.K., Hurst, J.E., Swift, B.L., Kirsch, A., Farquhar, J.F., Decker, D.J., and Siemer, W.F., 2016, Structured decision making as a framework for large-scale wildlife harvest management decisions: Ecosphere, v. 7, no. 12, Article e01613; 14 p., https://doi.org/10.1002/ecs2.1613.","productDescription":"Article e01613; 14 p.","ipdsId":"IP-078686","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470346,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1613","text":"Publisher Index Page"},{"id":348207,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"12","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-20","publicationStatus":"PW","scienceBaseUri":"5a003151e4b0531197b5a750","contributors":{"authors":[{"text":"Robinson, Kelly F.","contributorId":140157,"corporation":false,"usgs":false,"family":"Robinson","given":"Kelly F.","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false},{"id":13267,"text":"Warnell School of Forestry and Natural Resources, University of Georgia","active":true,"usgs":false},{"id":473,"text":"New York Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":719792,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719791,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hurst, Jeremy E.","contributorId":177504,"corporation":false,"usgs":false,"family":"Hurst","given":"Jeremy","email":"","middleInitial":"E.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":719793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swift, Bryan L.","contributorId":11433,"corporation":false,"usgs":false,"family":"Swift","given":"Bryan","email":"","middleInitial":"L.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":719794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kirsch, Arthur","contributorId":199698,"corporation":false,"usgs":false,"family":"Kirsch","given":"Arthur","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":719795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Farquhar, James F.","contributorId":150969,"corporation":false,"usgs":false,"family":"Farquhar","given":"James","email":"","middleInitial":"F.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":719796,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Decker, Daniel J.","contributorId":166906,"corporation":false,"usgs":false,"family":"Decker","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":719797,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Siemer, William F.","contributorId":192551,"corporation":false,"usgs":false,"family":"Siemer","given":"William","email":"","middleInitial":"F.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":719798,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70193635,"text":"70193635 - 2016 - Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering","interactions":[],"lastModifiedDate":"2017-11-13T14:58:13","indexId":"70193635","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3908,"text":"Royal Society Open Science","active":true,"publicationSubtype":{"id":10}},"title":"Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering","docAbstract":"Error-tolerant likelihood-based match calling presents a promising technique to accurately identify recapture events in genetic mark–recapture studies by combining probabilities of latent genotypes and probabilities of observed genotypes, which may contain genotyping errors. Combined with clustering algorithms to group samples into sets of recaptures based upon pairwise match calls, these tools can be used to reconstruct accurate capture histories for mark–recapture modelling. Here, we assess the performance of a recently introduced error-tolerant likelihood-based match-calling model and sample clustering algorithm for genetic mark–recapture studies. We assessed both biallelic (i.e. single nucleotide polymorphisms; SNP) and multiallelic (i.e. microsatellite; MSAT) markers using a combination of simulation analyses and case study data on Pacific walrus (Odobenus rosmarus divergens) and fishers (Pekania pennanti). A novel two-stage clustering approach is demonstrated for genetic mark–recapture applications. First, repeat captures within a sampling occasion are identified. Subsequently, recaptures across sampling occasions are identified. The likelihood-based matching protocol performed well in simulation trials, demonstrating utility for use in a wide range of genetic mark–recapture studies. Moderately sized SNP (64+) and MSAT (10–15) panels produced accurate match calls for recaptures and accurate non-match calls for samples from closely related individuals in the face of low to moderate genotyping error. Furthermore, matching performance remained stable or increased as the number of genetic markers increased, genotyping error notwithstanding.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rsos.160457","usgsCitation":"Sethi, S., Linden, D., Wenburg, J., Lewis, C., Lemons, P.R., Fuller, A.K., and Hare, M.P., 2016, Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering: Royal Society Open Science, v. 3, p. 1-14, https://doi.org/10.1098/rsos.160457.","productDescription":"Article 160457; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-076769","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470349,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsos.160457","text":"Publisher Index Page"},{"id":348725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc7de4b06e28e9c23f09","contributors":{"authors":[{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719697,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Linden, Daniel","contributorId":199671,"corporation":false,"usgs":false,"family":"Linden","given":"Daniel","affiliations":[],"preferred":false,"id":719698,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wenburg, John","contributorId":199672,"corporation":false,"usgs":false,"family":"Wenburg","given":"John","affiliations":[],"preferred":false,"id":719699,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lewis, Cara","contributorId":199673,"corporation":false,"usgs":false,"family":"Lewis","given":"Cara","affiliations":[],"preferred":false,"id":719700,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lemons, Patrick R.","contributorId":11014,"corporation":false,"usgs":true,"family":"Lemons","given":"Patrick","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":719701,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719703,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hare, Matthew P.","contributorId":171454,"corporation":false,"usgs":false,"family":"Hare","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":719702,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70175667,"text":"70175667 - 2016 - An experimental study of the role of subsurface plumbing on geothermal discharge","interactions":[],"lastModifiedDate":"2016-12-30T09:50:21","indexId":"70175667","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"An experimental study of the role of subsurface plumbing on geothermal discharge","docAbstract":"In order to better understand the diverse discharge styles and eruption intervals observed at geothermal features, we performed three series of laboratory experiments with differing plumbing geometries. A single, straight conduit that connects a hot water bath (flask) to a vent (funnel) can originate geyser-like periodic eruptions, continuous discharge like a boiling spring, and fumarole-like steam discharge, depending on the conduit length and radius. The balance between the heat loss from the conduit walls and the heat supplied from the bottom determines whether and where water can condense which in turn controls discharge style. Next, we connected the conduit to a cold water reservoir through a branch, simulating the inflow from an external water source. Colder water located at a higher elevation than a branching point can flow into the conduit to stop the boiling in the flask, controlling the periodicity of the eruption. When an additional branch is connected to a second cold water reservoir, the two cold reservoirs can interact. Our experiments show that branching allows new processes to occur, such as recharge of colder water and escape of steam from side channels, leading to greater variation in discharge styles and eruption intervals. This model is consistent with the fact that eruption duration is not controlled by emptying reservoirs. We show how differences in plumbing geometries can explain various discharge styles and eruption intervals observed in El Tatio, Chile, and Yellowstone, USA.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016GC006472","usgsCitation":"Namiki, A., Ueno, Y., Hurwitz, S., Manga, M., Munoz-Saez, C., and Murphy, F., 2016, An experimental study of the role of subsurface plumbing on geothermal discharge: Geochemistry, Geophysics, Geosystems, v. 17, no. 9, p. 3691-3716, https://doi.org/10.1002/2016GC006472.","productDescription":"26 p.","startPage":"3691","endPage":"3716","ipdsId":"IP-078999","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":470394,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1480735","text":"Publisher Index Page"},{"id":332596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-15","publicationStatus":"PW","scienceBaseUri":"5864dd4ee4b0cd2dabe7c1cd","contributors":{"authors":[{"text":"Namiki, Atsuko","contributorId":131170,"corporation":false,"usgs":false,"family":"Namiki","given":"Atsuko","email":"","affiliations":[{"id":7267,"text":"University of Tokyo","active":true,"usgs":false}],"preferred":false,"id":645983,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ueno, Yoshinori","contributorId":173811,"corporation":false,"usgs":false,"family":"Ueno","given":"Yoshinori","email":"","affiliations":[{"id":27300,"text":"Hiroshima University","active":true,"usgs":false}],"preferred":false,"id":645984,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":645982,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Manga, Michael","contributorId":84679,"corporation":false,"usgs":true,"family":"Manga","given":"Michael","affiliations":[],"preferred":false,"id":645985,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Munoz-Saez, Carolina","contributorId":131167,"corporation":false,"usgs":false,"family":"Munoz-Saez","given":"Carolina","affiliations":[{"id":7102,"text":"University of California, Berkeley, Dept. of Civil & Envir. Engineering","active":true,"usgs":false}],"preferred":false,"id":645986,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murphy, Fred fmurphy@usgs.gov","contributorId":4572,"corporation":false,"usgs":true,"family":"Murphy","given":"Fred","email":"fmurphy@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":645987,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70187247,"text":"70187247 - 2016 - Canada goose nest survival at rural wetlands in north-central Iowa","interactions":[],"lastModifiedDate":"2017-04-28T13:16:43","indexId":"70187247","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Canada goose nest survival at rural wetlands in north-central Iowa","docAbstract":"The last comprehensive nest survival study of the breeding giant Canada goose (Branta canadensis maxima) population in Iowa, USA, was conducted >30 years ago during a period of population recovery, during which available nesting habitat consisted primarily of artificial nest structures. Currently, Iowa's resident goose population is stable and nests in a variety of habitats. We analyzed the effects of available habitat on nest survival and how nest survival rates compared with those of the expanding goose population studied previously to better understand how to maintain a sustainable Canada goose population in Iowa. We documented Canada goose nest survival at rural wetland sites in north-central Iowa. We monitored 121 nests in 2013 and 149 nests in 2014 at 5 Wildlife Management Areas (WMAs) with various nesting habitats, including islands, muskrat (Ondatra zibethicus) houses, and elevated nest structures. We estimated daily nest-survival rate using the nest survival model in Program MARK. Survival was influenced by year, site, stage, presence of a camera, nest age, and an interaction between nest age and stage. Nest success rates for the 28-day incubation period by site and year combination ranged from 0.10 to 0.84. Nest survival was greatest at sites with nest structures (β = 17.34). Nest survival was negatively affected by lowered water levels at Rice Lake WMA (2013 β = −0.77, nest age β = −0.07). Timing of water-level drawdowns for shallow lake restorations may influence nest survival rates.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.716","usgsCitation":"Ness, B.N., and Klaver, R.W., 2016, Canada goose nest survival at rural wetlands in north-central Iowa: Wildlife Society Bulletin, v. 40, no. 4, p. 705-713, https://doi.org/10.1002/wsb.716.","productDescription":"9 p.","startPage":"705","endPage":"713","ipdsId":"IP-066674","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":470366,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/222","text":"External Repository"},{"id":340615,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","otherGeospatial":"Cerro Gordo County, Hancock County, Winnebago County, Worth County, Wright County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-93.0242,43.2564],[-93.0245,43.2122],[-93.0242,43.1695],[-93.0241,43.0826],[-93.0241,42.9939],[-93.026,42.9071],[-93.1455,42.9075],[-93.2637,42.9073],[-93.3813,42.9074],[-93.4989,42.9075],[-93.4996,42.8184],[-93.4996,42.7306],[-93.4996,42.6434],[-93.5002,42.557],[-93.6191,42.5565],[-93.7367,42.5568],[-93.8563,42.557],[-93.972,42.5566],[-93.9714,42.6434],[-93.9714,42.7307],[-93.9713,42.8184],[-93.9713,42.9066],[-93.9713,42.9926],[-93.9713,43.0822],[-93.97,43.1691],[-93.9699,43.2573],[-93.9705,43.3447],[-93.9699,43.4334],[-93.9691,43.5044],[-93.6782,43.5047],[-93.6485,43.5045],[-93.4964,43.504],[-93.2844,43.5032],[-93.0502,43.5034],[-93.0238,43.5035],[-93.0242,43.432],[-93.0242,43.3442],[-93.0242,43.2564]]]},\"properties\":{\"name\":\"Cerro Gordo\",\"state\":\"IA\"}}]}","volume":"40","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-25","publicationStatus":"PW","scienceBaseUri":"590454a3e4b022cee40dc22a","contributors":{"authors":[{"text":"Ness, Brenna N.","contributorId":191566,"corporation":false,"usgs":false,"family":"Ness","given":"Brenna","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":693488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":693105,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178570,"text":"70178570 - 2016 - Graphical function mapping as a new way to explore cause-and-effect chains","interactions":[],"lastModifiedDate":"2018-02-28T14:36:31","indexId":"70178570","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1657,"text":"Fisheries","onlineIssn":"1548-8446","printIssn":"0363-2415","active":true,"publicationSubtype":{"id":10}},"title":"Graphical function mapping as a new way to explore cause-and-effect chains","docAbstract":"Graphical function mapping provides a simple method for improving communication within interdisciplinary research teams and between scientists and nonscientists. This article introduces graphical function mapping using two examples and discusses its usefulness. Function mapping projects the outcome of one function into another to show the combined effect. Using this mathematical property in a simpler, even cartoon-like, graphical way allows the rapid combination of multiple information sources (models, empirical data, expert judgment, and guesses) in an intuitive visual to promote further discussion, scenario development, and clear communication.
","language":"English","publisher":"American Fisheries Society","publisherLocation":"Bethesda, MD","doi":"10.1080/03632415.2016.1221404","usgsCitation":"Evans, M.A., 2016, Graphical function mapping as a new way to explore cause-and-effect chains: Fisheries, v. 41, no. 11, p. 638-643, https://doi.org/10.1080/03632415.2016.1221404.","productDescription":"6 p.","startPage":"638","endPage":"643","ipdsId":"IP-060085","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":331372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"11","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-04","publicationStatus":"PW","scienceBaseUri":"584144dde4b04fc80e50737f","contributors":{"authors":[{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":4883,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":654409,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70193163,"text":"70193163 - 2016 - Bayesian analysis of Jolly-Seber type models","interactions":[],"lastModifiedDate":"2017-11-20T15:57:21","indexId":"70193163","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1573,"text":"Environmental and Ecological Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Bayesian analysis of Jolly-Seber type models","docAbstract":"We propose the use of finite mixtures of continuous distributions in modelling the process by which new individuals, that arrive in groups, become part of a wildlife population. We demonstrate this approach using a data set of migrating semipalmated sandpipers (Calidris pussila) for which we extend existing stopover models to allow for individuals to have different behaviour in terms of their stopover duration at the site. We demonstrate the use of reversible jump MCMC methods to derive posterior distributions for the model parameters and the models, simultaneously. The algorithm moves between models with different numbers of arrival groups as well as between models with different numbers of behavioural groups. The approach is shown to provide new ecological insights about the stopover behaviour of semipalmated sandpipers but is generally applicable to any population in which animals arrive in groups and potentially exhibit heterogeneity in terms of one or more other processes.
","language":"English","publisher":"Springer","doi":"10.1007/s10651-016-0352-0","usgsCitation":"Matechou, E., Nicholls, G.K., Morgan, B.J., Collazo, J., and Lyons, J.E., 2016, Bayesian analysis of Jolly-Seber type models: Environmental and Ecological Statistics, v. 23, no. 4, p. 531-547, https://doi.org/10.1007/s10651-016-0352-0.","productDescription":"17 p.","startPage":"531","endPage":"547","ipdsId":"IP-057563","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":470376,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10651-016-0352-0","text":"Publisher Index Page"},{"id":349161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-04","publicationStatus":"PW","scienceBaseUri":"5a60fc7de4b06e28e9c23f0f","contributors":{"authors":[{"text":"Matechou, Eleni","contributorId":200631,"corporation":false,"usgs":false,"family":"Matechou","given":"Eleni","email":"","affiliations":[],"preferred":false,"id":722930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nicholls, Geoff K.","contributorId":200632,"corporation":false,"usgs":false,"family":"Nicholls","given":"Geoff","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":722931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morgan, Byron J. T.","contributorId":200633,"corporation":false,"usgs":false,"family":"Morgan","given":"Byron","email":"","middleInitial":"J. T.","affiliations":[],"preferred":false,"id":722932,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime A. 0000-0002-1816-7744 jaime_collazo@usgs.gov","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":173448,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime A.","email":"jaime_collazo@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":718111,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lyons, James E. 0000-0002-9810-8751 jelyons@usgs.gov","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":177546,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"jelyons@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":722933,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70185004,"text":"70185004 - 2016 - Latent spatial models and sampling design for landscape genetics","interactions":[],"lastModifiedDate":"2017-11-27T11:01:49","indexId":"70185004","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":787,"text":"Annals of Applied Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Latent spatial models and sampling design for landscape genetics","docAbstract":"We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.
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Conversely, the scientific community lacks methods for identifying which advancements in system understanding would have the highest value to decision-makers. A case in point is barrier island restoration, where decision-makers lack tools to objectively use system understanding to determine how to optimally use limited contingency funds when project construction in this dynamic environment does not proceed as expected. In this study, collaborative structured decision-making (SDM) was evaluated as an approach to incorporate process understanding into mid-construction decisions and to identify priority gaps in knowledge from a management perspective. The focus was a barrier island restoration project at Ship Island, Mississippi, where sand will be used to close an extensive breach that currently divides the island. SDM was used to estimate damage that may occur during construction, and guide repair decisions within the confines of limited availability of sand and funding to minimize adverse impacts to project objectives. Sand was identified as more limiting than funds, and unrepaired major breaching would negatively impact objectives. Repairing minor damage immediately was determined to be generally more cost effective (depending on the longshore extent) than risking more damage to a weakened project. Key gaps in process-understanding relative to project management were identified as the relationship of island width to breach formation; the amounts of sand lost during breaching, lowering, or narrowing of the berm; the potential for minor breaches to self-heal versus developing into a major breach; and the relationship between upstream nourishment and resiliency of the berm to storms. This application is a prototype for using structured decision-making in support of engineering projects in dynamic environments where mid-construction decisions may arise; highlights uncertainty about barrier island physical processes that limit the ability to make robust decisions; and demonstrates the potential for direct incorporation of process-based models in a formal adaptive management decision framework.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2016.08.078","usgsCitation":"Dalyander, P.S., Meyers, M.B., Mattsson, B., Steyer, G., Godsey, E., McDonald, J., Byrnes, M.R., and Ford, M., 2016, Use of structured decision-making to explicitly incorporate environmental process understanding in management of coastal restoration projects: Case study on barrier islands of the northern Gulf of Mexico: Journal of Environmental Management, v. 183, no. 3, p. 497-509, https://doi.org/10.1016/j.jenvman.2016.08.078.","productDescription":"13 p.","startPage":"497","endPage":"509","ipdsId":"IP-068842","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470460,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2016.08.078","text":"Publisher Index Page"},{"id":331388,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"East Ship Island, West Ship Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.000244140625,\n 30.18994745521063\n ],\n [\n -89.000244140625,\n 30.267370168467806\n ],\n [\n -88.85639190673828,\n 30.267370168467806\n ],\n [\n -88.85639190673828,\n 30.18994745521063\n ],\n [\n -89.000244140625,\n 30.18994745521063\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"183","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"584144dce4b04fc80e507373","contributors":{"authors":[{"text":"Dalyander, P. Soupy 0000-0001-9583-0872 sdalyander@usgs.gov","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":141015,"corporation":false,"usgs":true,"family":"Dalyander","given":"P.","email":"sdalyander@usgs.gov","middleInitial":"Soupy","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":654607,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyers, Michelle B. 0000-0002-5937-1012 mmeyers@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-1012","contributorId":5608,"corporation":false,"usgs":true,"family":"Meyers","given":"Michelle","email":"mmeyers@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":654608,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mattsson, Brady","contributorId":59692,"corporation":false,"usgs":true,"family":"Mattsson","given":"Brady","affiliations":[],"preferred":false,"id":654609,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steyer, Gregory 0000-0001-7231-0110","orcid":"https://orcid.org/0000-0001-7231-0110","contributorId":27797,"corporation":false,"usgs":true,"family":"Steyer","given":"Gregory","affiliations":[],"preferred":false,"id":654610,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Godsey, Elizabeth","contributorId":177095,"corporation":false,"usgs":false,"family":"Godsey","given":"Elizabeth","affiliations":[],"preferred":false,"id":654611,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDonald, Justin","contributorId":171407,"corporation":false,"usgs":false,"family":"McDonald","given":"Justin","email":"","affiliations":[{"id":26898,"text":"University of Auckland, New Zealand","active":true,"usgs":false}],"preferred":false,"id":654612,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Byrnes, Mark R.","contributorId":102504,"corporation":false,"usgs":true,"family":"Byrnes","given":"Mark","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":654613,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ford, Mark","contributorId":177097,"corporation":false,"usgs":false,"family":"Ford","given":"Mark","email":"","affiliations":[],"preferred":false,"id":654614,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70182771,"text":"70182771 - 2016 - Evolutionary traps as keys to understanding behavioral maladaptation","interactions":[],"lastModifiedDate":"2017-03-01T12:36:53","indexId":"70182771","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5307,"text":"Current Opinion in Behavioral Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Evolutionary traps as keys to understanding behavioral maladaptation","docAbstract":"Evolutionary traps are severe cases of behavioral maladaptation that occur when, due to human activity, the cues animals use to guide their behavior become uncoupled from their fitness consequences. The result is that animals can prefer the most dangerous resources or behaviors, even when better options are available. Traps are increasingly common and represent a significant wildlife conservation problem. Understanding of the more proximate sensory-cognitive mechanisms underpinning traps remains poor, which highlights the need for interdisciplinary and collaborative approaches to investigating traps. Key to advancing basic trap theory and its conservation applications will be the development of appropriate and tractable model systems to investigate the mechanisms that cause traps within species, and how mechanisms vary across species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cobeha.2016.08.007","usgsCitation":"Robertson, B.A., and Chalfoun, A., 2016, Evolutionary traps as keys to understanding behavioral maladaptation: Current Opinion in Behavioral Sciences, v. 12, p. 12-17, https://doi.org/10.1016/j.cobeha.2016.08.007.","productDescription":"6 p.","startPage":"12","endPage":"17","ipdsId":"IP-074196","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":336749,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b7eba5e4b01ccd5500baef","contributors":{"authors":[{"text":"Robertson, Bruce A.","contributorId":171947,"corporation":false,"usgs":false,"family":"Robertson","given":"Bruce","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":680422,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chalfoun, Anna","contributorId":184161,"corporation":false,"usgs":true,"family":"Chalfoun","given":"Anna","affiliations":[],"preferred":false,"id":673693,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178358,"text":"70178358 - 2016 - The 2016 groundwater flow model for Dane County, Wisconsin","interactions":[],"lastModifiedDate":"2017-01-03T14:13:59","indexId":"70178358","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":242,"text":"Bulletin","active":false,"publicationSubtype":{"id":4}},"seriesNumber":"110","title":"The 2016 groundwater flow model for Dane County, Wisconsin","docAbstract":"A new groundwater flow model for Dane County, Wisconsin, replaces an earlier model developed in the 1990s by the Wisconsin Geological and Natural History Survey (WGNHS) and the U.S. Geological Survey (USGS). This modeling study was conducted cooperatively by the WGNHS and the USGS with funding from the Capital Area Regional Planning Commission (CARPC). Although the overall conceptual model of the groundwater system remains largely unchanged, the incorporation of newly acquired high-quality datasets, recent research findings, and improved modeling and calibration techniques have led to the development of a more detailed and sophisticated model representation of the groundwater system. The new model is three-dimensional and transient, and conceptualizes the county’s hydrogeology as a 12-layer system including all major unlithified and bedrock hydrostratigraphic units and two high-conductivity horizontal fracture zones.
Beginning from the surface down, the model represents the unlithified deposits as two distinct model layers (1 and 2). A single layer (3) simulates the Ordovician sandstone and dolomite of the Sinnipee, Ancell, and Prairie du Chien Groups. Sandstone of the Jordan Formation (layer 4) and silty dolostone of the St. Lawrence Formation (layer 5) each comprise separate model layers. The underlying glauconitic sandstone of the Tunnel City Group makes up three distinct layers: an upper aquifer (layer 6), a fracture feature (layer 7), and a lower aquifer (layer 8). The fracture layer represents a network of horizontal bedding-plane fractures that serve as a preferential pathway for groundwater flow. The model simulates the sandstone of the Wonewoc Formation as an upper aquifer (layer 9) with a bedding-plane fracture feature (layer 10) at its base. The Eau Claire aquitard (layer 11) includes shale beds within the upper portion of the Eau Claire Formation. This layer, along with overlying bedrock units, is mostly absent in the preglacially eroded valleys along the Yahara River valley and in northeastern Dane County. Layer 12 represents the Mount Simon sandstone as the lowermost model layer. It directly overlies the Precambrian crystalline basement rock, whose top surface forms the lower boundary of the model.
The model uses the USGS MODFLOW-NWT finite-difference code, a standalone version of MODFLOW-2005 that incorporates the Newton (NWT) solver. MODFLOW-NWT improves the handling of unconfined conditions by smoothing the transition from wet to dry cells. The model explicitly simulates groundwater–surface-water interaction with streamflow routing and lake-level fluctuation. Model input included published and unpublished hydrogeologic data from recent estimates of aquifer hydraulic conductivities. A spatial groundwater recharge distribution was obtained from a recent GIS-based, soil-water-balance model for Dane County. Groundwater withdrawals from pumping were simulated for 572 wells across the entire model domain, which includes Dane County and portions of seven neighboring counties—Columbia, Dodge, Green, Iowa, Jefferson, Lafayette, and Rock. These wells withdrew an average of 60 million gallons per day (mgd) over the 5-year period from 2006 through 2010. Within Dane County, 385 wells were simulated with an average withdrawal rate of 52 mgd.
Model calibration used the parameter estimation code PEST, and calibration targets included heads, stream and spring flows, lake levels, and borehole flows. Steady-state calibration focused on the period 2006 through 2010; the transient calibration focused on the 7-week drought period from late May through July 2012.
This model represents a significant step forward from previous work because of its finer grid resolution, improved hydrostratigraphic discretization, transient capabilities, and more sophisticated representation of surface-water features and multi-aquifer wells.
Potential applications of the model include evaluation of potential sites for and impacts of new high-capacity wells, development of wellhead protection plans, evaluating the effects of changing land use and climate on groundwater, and quantifying the relationships between groundwater and surface water.
","language":"English","publisher":"Wisconsin Geological and Natural History Survey","isbn":"978-0-88169-992-0","usgsCitation":"Parsen, M.J., Bradbury, K.R., Hunt, R.J., and Feinstein, D.T., 2016, The 2016 groundwater flow model for Dane County, Wisconsin: Bulletin 110, 56 p.","productDescription":"56 p.","numberOfPages":"64","ipdsId":"IP-071783","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":332790,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":330992,"type":{"id":15,"text":"Index Page"},"url":"https://wgnhs.uwex.edu/dane-county-groundwater-model/"}],"country":"United States","state":"Wisconsin","county":"Dane County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-89.0094,43.286],[-89.0084,43.2555],[-89.0094,43.2],[-89.01,43.1131],[-89.0109,43.0849],[-89.0107,43.0271],[-89.0132,42.9353],[-89.013,42.8762],[-89.0119,42.8471],[-89.132,42.8479],[-89.2488,42.8478],[-89.3689,42.8484],[-89.3688,42.8575],[-89.4832,42.858],[-89.6026,42.8575],[-89.7196,42.8587],[-89.8377,42.8598],[-89.8375,42.9471],[-89.8386,43.0317],[-89.8384,43.1181],[-89.8394,43.205],[-89.8325,43.2123],[-89.825,43.2187],[-89.8175,43.226],[-89.8125,43.2342],[-89.8088,43.2369],[-89.8012,43.2365],[-89.7874,43.2356],[-89.771,43.237],[-89.7579,43.2379],[-89.7529,43.2443],[-89.7485,43.2507],[-89.7391,43.2548],[-89.7259,43.2644],[-89.7171,43.2739],[-89.714,43.2821],[-89.7165,43.2867],[-89.7235,43.2935],[-89.7209,43.2935],[-89.6008,43.2932],[-89.4819,43.2942],[-89.3617,43.2954],[-89.3624,43.2832],[-89.246,43.2834],[-89.1271,43.2827],[-89.0094,43.286]]]},\"properties\":{\"name\":\"Dane\",\"state\":\"WI\"}}]}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"586cc695e4b0f5ce109fa953","contributors":{"authors":[{"text":"Parsen, Michael J.","contributorId":176845,"corporation":false,"usgs":false,"family":"Parsen","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":657411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradbury, Kenneth R.","contributorId":49419,"corporation":false,"usgs":true,"family":"Bradbury","given":"Kenneth","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":657412,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunt, Randall J. 0000-0001-6465-9304 rjhunt@usgs.gov","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":1129,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall","email":"rjhunt@usgs.gov","middleInitial":"J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":657413,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feinstein, Daniel T. 0000-0003-1151-2530 dtfeinst@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-2530","contributorId":1907,"corporation":false,"usgs":true,"family":"Feinstein","given":"Daniel","email":"dtfeinst@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":657414,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192877,"text":"70192877 - 2016 - Panel regressions to estimate low-flow response to rainfall variability in ungaged basins","interactions":[],"lastModifiedDate":"2018-04-03T11:40:41","indexId":"70192877","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Panel regressions to estimate low-flow response to rainfall variability in ungaged basins","docAbstract":"Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.
","language":"English","publisher":"AGU","doi":"10.1002/2016WR018718","usgsCitation":"Bassiouni, M., Vogel, R.M., and Archfield, S.A., 2016, Panel regressions to estimate low-flow response to rainfall variability in ungaged basins: Water Resources Research, v. 52, no. 12, p. 9470-9494, https://doi.org/10.1002/2016WR018718.","productDescription":"25 p.","startPage":"9470","endPage":"9494","ipdsId":"IP-064919","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":470377,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr018718","text":"Publisher Index Page"},{"id":349233,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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the Upper Devonian-Lower Mississippian Bakken Formation in the northern Williston Basin, Saskatchewan, southwestern Manitoba, and southeastern Alberta, Canada","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Hydrocarbon source rocks in unconventional plays, Rocky Mountain region: RMAG guidebook","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Rocky Mountain Association of Geologists","usgsCitation":"Higley, D.K., and Gianoutsos, N.J., 2016, Petroleum system model of the Upper Devonian-Lower Mississippian Bakken Formation in the northern Williston Basin, Saskatchewan, southwestern Manitoba, and southeastern Alberta, Canada, chap. of Hydrocarbon source rocks in unconventional plays, Rocky Mountain region: RMAG guidebook, p. 172-189.","productDescription":"18 p.","startPage":"172","endPage":"189","ipdsId":"IP-063119","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":333117,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.rmag.org/publications"},{"id":333118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5878a48ce4b04df303d95812","contributors":{"authors":[{"text":"Higley, Debra K. 0000-0001-8024-9954 higley@usgs.gov","orcid":"https://orcid.org/0000-0001-8024-9954","contributorId":152663,"corporation":false,"usgs":true,"family":"Higley","given":"Debra","email":"higley@usgs.gov","middleInitial":"K.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":656024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gianoutsos, Nicholas J. 0000-0002-6510-6549 ngianoutsos@usgs.gov","orcid":"https://orcid.org/0000-0002-6510-6549","contributorId":3607,"corporation":false,"usgs":true,"family":"Gianoutsos","given":"Nicholas","email":"ngianoutsos@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":656025,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70189332,"text":"70189332 - 2016 - Climate-induced warming of lakes can be either amplified or suppressed by trends in water clarity","interactions":[],"lastModifiedDate":"2017-07-11T13:16:58","indexId":"70189332","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5456,"text":"Limnology and Oceanography Letters","active":true,"publicationSubtype":{"id":10}},"title":"Climate-induced warming of lakes can be either amplified or suppressed by trends in water clarity","docAbstract":"Climate change is rapidly warming aquatic ecosystems including lakes and reservoirs. However, variability in lake characteristics can modulate how lakes respond to climate. Water clarity is especially important both because it influences the depth range over which heat is absorbed, and because it is changing in many lakes. Here, we show that simulated long-term water clarity trends influence how both surface and bottom water temperatures of lakes and reservoirs respond to climate change. Clarity changes can either amplify or suppress climate-induced warming, depending on lake depth and the direction of clarity change. Using a process-based model to simulate 1894 north temperate lakes from 1979 to 2012, we show that a scenario of decreasing clarity at a conservative yet widely observed rate of 0.92% yr−1 warmed surface waters and cooled bottom waters at rates comparable in magnitude to climate-induced warming. For lakes deeper than 6.5 m, decreasing clarity was sufficient to fully offset the effects of climate-induced warming on median whole-lake mean temperatures. Conversely, a scenario increasing clarity at the same rate cooled surface waters and warmed bottom waters relative to baseline warming rates. Furthermore, in 43% of lakes, increasing clarity more than doubled baseline bottom temperature warming rates. Long-term empirical observations of water temperature in lakes with and without clarity trends support these simulation results. Together, these results demonstrate that water clarity trends may be as important as rising air temperatures in determining how waterbodies respond to climate change.
","language":"English","publisher":"ASLO","doi":"10.1002/lol2.10027","usgsCitation":"Rose, K.C., Winslow, L.A., Read, J.S., and Hansen, G.J., 2016, Climate-induced warming of lakes can be either amplified or suppressed by trends in water clarity: Limnology and Oceanography Letters, v. 1, no. 1, p. 44-53, https://doi.org/10.1002/lol2.10027.","productDescription":"10 p.","startPage":"44","endPage":"53","ipdsId":"IP-070817","costCenters":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"links":[{"id":470371,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lol2.10027","text":"Publisher Index Page"},{"id":438499,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7028PN4","text":"USGS data release","linkHelpText":"Climate warming of Wisconsin lakes can be either amplified or suppressed by trends in water clarity"},{"id":343576,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","issue":"1","noUsgsAuthors":false,"publicationDate":"2016-10-24","publicationStatus":"PW","scienceBaseUri":"5965b26be4b0d1f9f05b37f1","contributors":{"authors":[{"text":"Rose, Kevin C.","contributorId":174809,"corporation":false,"usgs":false,"family":"Rose","given":"Kevin","email":"","middleInitial":"C.","affiliations":[{"id":12656,"text":"Rensselaer Polytechnic Institute","active":true,"usgs":false}],"preferred":false,"id":704200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winslow, Luke A. 0000-0002-8602-5510 lwinslow@usgs.gov","orcid":"https://orcid.org/0000-0002-8602-5510","contributorId":5919,"corporation":false,"usgs":true,"family":"Winslow","given":"Luke","email":"lwinslow@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":false,"id":704201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":704202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Gretchen J. A.","contributorId":131099,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen","email":"","middleInitial":"J. A.","affiliations":[{"id":7242,"text":"Wisconsin Department of Natural Resources, Madison, WI, USA","active":true,"usgs":false}],"preferred":false,"id":704203,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179636,"text":"70179636 - 2016 - mizuRoute version 1: A river network routing tool for a continental domain water resources applications","interactions":[],"lastModifiedDate":"2017-01-09T11:33:05","indexId":"70179636","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"mizuRoute version 1: A river network routing tool for a continental domain water resources applications","docAbstract":"This paper describes the first version of a stand-alone runoff routing tool, mizuRoute. The mizuRoute tool post-processes runoff outputs from any distributed hydrologic model or land surface model to produce spatially distributed streamflow at various spatial scales from headwater basins to continental-wide river systems. The tool can utilize both traditional grid-based river network and vector-based river network data. Both types of river network include river segment lines and the associated drainage basin polygons, but the vector-based river network can represent finer-scale river lines than the grid-based network. Streamflow estimates at any desired location in the river network can be easily extracted from the output of mizuRoute. The routing process is simulated as two separate steps. First, hillslope routing is performed with a gamma-distribution-based unit-hydrograph to transport runoff from a hillslope to a catchment outlet. The second step is river channel routing, which is performed with one of two routing scheme options: (1) a kinematic wave tracking (KWT) routing procedure; and (2) an impulse response function – unit-hydrograph (IRF-UH) routing procedure. The mizuRoute tool also includes scripts (python, NetCDF operators) to pre-process spatial river network data. This paper demonstrates mizuRoute's capabilities to produce spatially distributed streamflow simulations based on river networks from the United States Geological Survey (USGS) Geospatial Fabric (GF) data set in which over 54 000 river segments and their contributing areas are mapped across the contiguous United States (CONUS). A brief analysis of model parameter sensitivity is also provided. The mizuRoute tool can assist model-based water resources assessments including studies of the impacts of climate change on streamflow.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-9-2223-2016","usgsCitation":"Mizukami, N., Clark, M.P., Sampson, K., Nijssen, B., Mao, Y., McMillan, H., Viger, R.J., Markstrom, S.L., Hay, L.E., Woods, R., Arnold, J.R., and Brekke, L.D., 2016, mizuRoute version 1: A river network routing tool for a continental domain water resources applications: Geoscientific Model Development, v. 9, p. 2223-2238, https://doi.org/10.5194/gmd-9-2223-2016.","productDescription":"16 p.","startPage":"2223","endPage":"2238","ipdsId":"IP-075055","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":470378,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-9-2223-2016","text":"Publisher Index Page"},{"id":332987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-23","publicationStatus":"PW","scienceBaseUri":"5874b0ade4b0a829a320bb67","contributors":{"authors":[{"text":"Mizukami, Naoki","contributorId":178120,"corporation":false,"usgs":false,"family":"Mizukami","given":"Naoki","email":"","affiliations":[],"preferred":false,"id":657982,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Martyn P.","contributorId":178121,"corporation":false,"usgs":false,"family":"Clark","given":"Martyn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":657983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sampson, Kevin","contributorId":178122,"corporation":false,"usgs":false,"family":"Sampson","given":"Kevin","email":"","affiliations":[],"preferred":false,"id":657984,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nijssen, Bart","contributorId":178123,"corporation":false,"usgs":false,"family":"Nijssen","given":"Bart","email":"","affiliations":[],"preferred":false,"id":657985,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mao, Yixin","contributorId":139783,"corporation":false,"usgs":false,"family":"Mao","given":"Yixin","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":657986,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McMillan, Hilary","contributorId":176321,"corporation":false,"usgs":false,"family":"McMillan","given":"Hilary","email":"","affiliations":[],"preferred":false,"id":657987,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Viger, Roland J. 0000-0003-2520-714X rviger@usgs.gov","orcid":"https://orcid.org/0000-0003-2520-714X","contributorId":168799,"corporation":false,"usgs":true,"family":"Viger","given":"Roland","email":"rviger@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":657988,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":146553,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven","email":"markstro@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":657989,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":657981,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Woods, Ross","contributorId":178124,"corporation":false,"usgs":false,"family":"Woods","given":"Ross","affiliations":[],"preferred":false,"id":657990,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Arnold, Jeffrey R.","contributorId":178125,"corporation":false,"usgs":false,"family":"Arnold","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":657991,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Brekke, Levi D.","contributorId":178126,"corporation":false,"usgs":false,"family":"Brekke","given":"Levi","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":657992,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70193722,"text":"70193722 - 2016 - A new strategy for earthquake focal mechanisms using waveform-correlation-derived relative polarities and cluster analysis: Application to the 2014 Long Valley Caldera earthquake swarm","interactions":[],"lastModifiedDate":"2017-11-04T13:27:11","indexId":"70193722","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"A new strategy for earthquake focal mechanisms using waveform-correlation-derived relative polarities and cluster analysis: Application to the 2014 Long Valley Caldera earthquake swarm","docAbstract":"In microseismicity analyses, reliable focal mechanisms can typically be obtained for only a small subset of located events. We address this limitation here, presenting a framework for determining robust focal mechanisms for entire populations of very small events. To achieve this, we resolve relative P and S wave polarities between pairs of waveforms by using their signed correlation coefficients—a by-product of previously performed precise earthquake relocation. We then use cluster analysis to group events with similar patterns of polarities across the network. Finally, we apply a standard mechanism inversion to the grouped data, using either catalog or correlation-derived P wave polarity data sets. This approach has great potential for enhancing analyses of spatially concentrated microseismicity such as earthquake swarms, mainshock-aftershock sequences, and industrial reservoir stimulation or injection-induced seismic sequences. To demonstrate its utility, we apply this technique to the 2014 Long Valley Caldera earthquake swarm. In our analysis, 85% of the events (7212 out of 8494 located by Shelly et al. [2016]) fall within five well-constrained mechanism clusters, more than 12 times the number with network-determined mechanisms. Of the earthquakes we characterize, 3023 (42%) have magnitudes smaller than 0.0. We find that mechanism variations are strongly associated with corresponding hypocentral structure, yet mechanism heterogeneity also occurs where it cannot be resolved by hypocentral patterns, often confined to small-magnitude events. Small (5–20°) rotations between mechanism orientations and earthquake location trends persist when we apply 3-D velocity models and might reflect a geometry of en echelon, interlinked shear, and dilational faulting.
","language":"English","publisher":"AGU","doi":"10.1002/2016JB013437","usgsCitation":"Shelly, D.R., Hardebeck, J.L., Ellsworth, W.L., and Hill, D.P., 2016, A new strategy for earthquake focal mechanisms using waveform-correlation-derived relative polarities and cluster analysis: Application to the 2014 Long Valley Caldera earthquake swarm: Journal of Geophysical Research B: Solid Earth, v. 121, no. 12, p. 8622-8641, https://doi.org/10.1002/2016JB013437.","productDescription":"20 p.","startPage":"8622","endPage":"8641","ipdsId":"IP-078358","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":348194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Long Valley Caldera","volume":"121","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-03","publicationStatus":"PW","scienceBaseUri":"59fedfb4e4b0531197b573c4","contributors":{"authors":[{"text":"Shelly, David R. dshelly@usgs.gov","contributorId":2978,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":720060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hardebeck, Jeanne L. 0000-0002-6737-7780 jhardebeck@usgs.gov","orcid":"https://orcid.org/0000-0002-6737-7780","contributorId":841,"corporation":false,"usgs":true,"family":"Hardebeck","given":"Jeanne","email":"jhardebeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":720061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellsworth, William L. ellsworth@usgs.gov","contributorId":787,"corporation":false,"usgs":true,"family":"Ellsworth","given":"William","email":"ellsworth@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":720062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hill, David P. hill@usgs.gov","contributorId":2600,"corporation":false,"usgs":true,"family":"Hill","given":"David","email":"hill@usgs.gov","middleInitial":"P.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":false,"id":720063,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70179091,"text":"70179091 - 2016 - Overpressure development through time using 4D pressure-volume-temperature modeling in the deep Anadarko Basin, Colorado, Kansas, Oklahoma, and Texas","interactions":[],"lastModifiedDate":"2018-01-08T13:16:40","indexId":"70179091","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Overpressure development through time using 4D pressure-volume-temperature modeling in the deep Anadarko Basin, Colorado, Kansas, Oklahoma, and Texas","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Hydrocarbon source rocks in unconventional plays, Rocky Mountain region: RMAG guidebook","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Rocky Mountain Association of Geologists","usgsCitation":"Higley, D.K., 2016, Overpressure development through time using 4D pressure-volume-temperature modeling in the deep Anadarko Basin, Colorado, Kansas, Oklahoma, and Texas, chap. of Hydrocarbon source rocks in unconventional plays, Rocky Mountain region: RMAG guidebook, p. 59-78.","productDescription":"19 p.","startPage":"59","endPage":"78","ipdsId":"IP-068623","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":333116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":333115,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.rmag.org/publications"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5878a48ce4b04df303d95810","contributors":{"authors":[{"text":"Higley, Debra K. 0000-0001-8024-9954 higley@usgs.gov","orcid":"https://orcid.org/0000-0001-8024-9954","contributorId":152663,"corporation":false,"usgs":true,"family":"Higley","given":"Debra","email":"higley@usgs.gov","middleInitial":"K.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":656026,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70184183,"text":"70184183 - 2016 - Topographic and fire weather controls of fire refugia in forested ecosystems of northwestern North America","interactions":[],"lastModifiedDate":"2018-03-27T09:54:46","indexId":"70184183","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Topographic and fire weather controls of fire refugia in forested ecosystems of northwestern North America","docAbstract":"Fire refugia, sometimes referred to as fire islands, shadows, skips, residuals, or fire remnants, are an important element of the burn mosaic, but we lack a quantitative framework that links observations of fire refugia from different environmental contexts. Here, we develop and test a conceptual model for how predictability of fire refugia varies according to topographic complexity and fire weather conditions. Refugia were quantified as areas unburned or burned at comparatively low severity based on remotely sensed burn severity data. We assessed the relationship between refugia and a suite of terrain-related explanatory metrics by fitting a collection of boosted regression tree models. The models were developed\nfor seven study fires that burned in conifer-dominated forested landscapes of the Western Cordillera of Canada between 2001 and 2014. We fit nine models, each for distinct levels of fire weather and terrain ruggedness. Our framework revealed that the predictability and abundance of fire refugia varied among these environmental settings. We observed highest predictability under moderate fire weather conditions and moderate terrain ruggedness (ROC-AUC = 0.77), and lowest predictability in flatter landscapes and under high fire weather conditions (ROC-AUC = 0.63–0.68). Catchment slope, local aspect, relative position, topographic wetness, topographic convergence, and local slope all contributed to discriminating where refugia occur but the relative importance of these topographic controls differed among environments. Our framework allows us to characterize the predictability of contemporary fire refugia across multiple environmental settings and provides important insights for ecosystem resilience, wildfire management, conservation planning, and climate change adaptation.","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.1632","usgsCitation":"Krawchuk, M.A., Haire, S.L., Coop, J.D., Parisien, M., Whitman, E., Chong, G.W., and Miller, C., 2016, Topographic and fire weather controls of fire refugia in forested ecosystems of northwestern North America: Ecosphere, v. 7, no. 12, p. 1-18, https://doi.org/10.1002/ecs2.1632.","productDescription":"18 p.","startPage":"1","endPage":"18","ipdsId":"IP-074382","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":462015,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1632","text":"Publisher Index Page"},{"id":336773,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-29","publicationStatus":"PW","scienceBaseUri":"58b7eba4e4b01ccd5500baeb","contributors":{"authors":[{"text":"Krawchuk, Meg A.","contributorId":187425,"corporation":false,"usgs":false,"family":"Krawchuk","given":"Meg","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":680389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haire, Sandra L.","contributorId":187426,"corporation":false,"usgs":false,"family":"Haire","given":"Sandra","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":680390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coop, Jonathan D.","contributorId":187427,"corporation":false,"usgs":false,"family":"Coop","given":"Jonathan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":680391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parisien, Marc-André","contributorId":187428,"corporation":false,"usgs":false,"family":"Parisien","given":"Marc-André","affiliations":[],"preferred":false,"id":680392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whitman, Ellen","contributorId":187429,"corporation":false,"usgs":false,"family":"Whitman","given":"Ellen","email":"","affiliations":[],"preferred":false,"id":680393,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chong, Geneva W. 0000-0003-3883-5153 geneva_chong@usgs.gov","orcid":"https://orcid.org/0000-0003-3883-5153","contributorId":419,"corporation":false,"usgs":true,"family":"Chong","given":"Geneva","email":"geneva_chong@usgs.gov","middleInitial":"W.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":680388,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Carol","contributorId":187430,"corporation":false,"usgs":false,"family":"Miller","given":"Carol","email":"","affiliations":[],"preferred":false,"id":680394,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70191724,"text":"70191724 - 2016 - MODIS imagery improves pest risk assessment: A case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA","interactions":[],"lastModifiedDate":"2017-10-25T12:30:57","indexId":"70191724","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1536,"text":"Environmental Entomology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"MODIS imagery improves pest risk assessment: A case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA","title":"MODIS imagery improves pest risk assessment: A case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA","docAbstract":"Wheat stem sawfly (Cephus cinctus Norton, Hymenoptera: Cephidae) has long been a significant insect pest of spring, and more recently, winter wheat in the northern Great Plains. Wheat stem sawfly was first observed infesting winter wheat in Colorado in 2010 and, subsequently, has spread rapidly throughout wheat production regions of the state. Here, we used maximum entropy modeling (MaxEnt) to generate habitat suitability maps in order to predict the risk of crop damage as this species spreads throughout the winter wheat-growing regions of Colorado. We identified environmental variables that influence the current distribution of wheat stem sawfly in the state and evaluated whether remotely sensed variables improved model performance. We used presence localities of C. cinctus and climatic, topographic, soils, and normalized difference vegetation index and enhanced vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery as environmental variables. All models had high performance in that they were successful in predicting suitable habitat for C. cinctus in its current distribution in eastern Colorado. The enhanced vegetation index for the month of April improved model performance and was identified as a top contributor to MaxEnt model. Soil clay percent at 0–5 cm, temperature seasonality, and precipitation seasonality were also associated with C. cinctus distribution in Colorado. The improved model performance resulting from integrating vegetation indices in our study demonstrates the ability of remote sensing technologies to enhance species distribution modeling. These risk maps generated can assist managers in planning control measures for current infestations and assess the future risk of C. cinctus establishment in currently uninfested regions.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ee/nvw095","usgsCitation":"Lestina, J., Cook, M., Kumar, S., Morisette, J.T., Ode, P.J., and Peirs, F., 2016, MODIS imagery improves pest risk assessment: A case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA: Environmental Entomology, v. 45, no. 6, p. 1343-1351, https://doi.org/10.1093/ee/nvw095.","productDescription":"9 p.","startPage":"1343","endPage":"1351","ipdsId":"IP-077680","costCenters":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"links":[{"id":347349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","volume":"45","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-22","publicationStatus":"PW","scienceBaseUri":"59f1a2a7e4b0220bbd9d9f72","contributors":{"authors":[{"text":"Lestina, Jordan","contributorId":197312,"corporation":false,"usgs":false,"family":"Lestina","given":"Jordan","email":"","affiliations":[],"preferred":false,"id":713173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cook, Maxwell","contributorId":197313,"corporation":false,"usgs":false,"family":"Cook","given":"Maxwell","email":"","affiliations":[],"preferred":false,"id":713174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kumar, Sunil","contributorId":195493,"corporation":false,"usgs":false,"family":"Kumar","given":"Sunil","affiliations":[],"preferred":false,"id":713175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":713176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ode, Paul J.","contributorId":197314,"corporation":false,"usgs":false,"family":"Ode","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":713177,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peirs, Frank","contributorId":197315,"corporation":false,"usgs":false,"family":"Peirs","given":"Frank","email":"","affiliations":[],"preferred":false,"id":713178,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191263,"text":"70191263 - 2016 - Trace elements at the intersection of marine biological and geochemical evolution","interactions":[],"lastModifiedDate":"2017-10-02T13:21:19","indexId":"70191263","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1431,"text":"Earth-Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Trace elements at the intersection of marine biological and geochemical evolution","docAbstract":"Life requires a wide variety of bioessential trace elements to act as structural components and reactive centers in metalloenzymes. These requirements differ between organisms and have evolved over geological time, likely guided in some part by environmental conditions. Until recently, most of what was understood regarding trace element concentrations in the Precambrian oceans was inferred by extrapolation, geochemical modeling, and/or genomic studies. However, in the past decade, the increasing availability of trace element and isotopic data for sedimentary rocks of all ages has yielded new, and potentially more direct, insights into secular changes in seawater composition – and ultimately the evolution of the marine biosphere. Compiled records of many bioessential trace elements (including Ni, Mo, P, Zn, Co, Cr, Se, and I) provide new insight into how trace element abundance in Earth's ancient oceans may have been linked to biological evolution. Several of these trace elements display redox-sensitive behavior, while others are redox-sensitive but not bioessential (e.g., Cr, U). Their temporal trends in sedimentary archives provide useful constraints on changes in atmosphere-ocean redox conditions that are linked to biological evolution, for example, the activity of oxygen-producing, photosynthetic cyanobacteria. In this review, we summarize available Precambrian trace element proxy data, and discuss how temporal trends in the seawater concentrations of specific trace elements may be linked to the evolution of both simple and complex life. We also examine several biologically relevant and/or redox-sensitive trace elements that have yet to be fully examined in the sedimentary rock record (e.g., Cu, Cd, W) and suggest several directions for future studies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2016.10.013","usgsCitation":"Robbins, L.J., Lalonde, S.V., Planavsky, N.J., Partin, C.A., Reinhard, C.T., Kendall, B., Scott, C., Hardisty, D.S., Gill, B.C., Alessi, D.S., Dupont, C.L., Saito, M.A., Crowe, S.A., Poulton, S.W., Bekker, A., Lyons, T.W., and Konhauser, K.O., 2016, Trace elements at the intersection of marine biological and geochemical evolution: Earth-Science Reviews, v. 163, p. 323-348, https://doi.org/10.1016/j.earscirev.2016.10.013.","productDescription":"26 p.","startPage":"323","endPage":"348","ipdsId":"IP-079724","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":470348,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10012/13782","text":"External Repository"},{"id":346313,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"163","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59d35027e4b05fe04cc34d59","contributors":{"authors":[{"text":"Robbins, Leslie J.","contributorId":196838,"corporation":false,"usgs":false,"family":"Robbins","given":"Leslie","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":711744,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lalonde, Stefan V.","contributorId":196839,"corporation":false,"usgs":false,"family":"Lalonde","given":"Stefan","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":711745,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Planavsky, Noah J.","contributorId":196840,"corporation":false,"usgs":false,"family":"Planavsky","given":"Noah","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":711746,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Partin, Camille A.","contributorId":196841,"corporation":false,"usgs":false,"family":"Partin","given":"Camille","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":711747,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reinhard, Christopher T.","contributorId":196842,"corporation":false,"usgs":false,"family":"Reinhard","given":"Christopher","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":711748,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kendall, Brian","contributorId":196843,"corporation":false,"usgs":false,"family":"Kendall","given":"Brian","email":"","affiliations":[],"preferred":false,"id":711749,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scott, Clint 0000-0003-2778-2711 clintonscott@usgs.gov","orcid":"https://orcid.org/0000-0003-2778-2711","contributorId":5332,"corporation":false,"usgs":true,"family":"Scott","given":"Clint","email":"clintonscott@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":711743,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hardisty, Dalton S.","contributorId":196844,"corporation":false,"usgs":false,"family":"Hardisty","given":"Dalton","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":711750,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gill, Benjamin C.","contributorId":196845,"corporation":false,"usgs":false,"family":"Gill","given":"Benjamin","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":711751,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Alessi, Daniel S.","contributorId":176793,"corporation":false,"usgs":false,"family":"Alessi","given":"Daniel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":711752,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Dupont, Christopher L.","contributorId":196846,"corporation":false,"usgs":false,"family":"Dupont","given":"Christopher","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":711753,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Saito, Mak A.","contributorId":196847,"corporation":false,"usgs":false,"family":"Saito","given":"Mak","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":711754,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Crowe, Sean A.","contributorId":196853,"corporation":false,"usgs":false,"family":"Crowe","given":"Sean","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":711769,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Poulton, Simon W.","contributorId":196848,"corporation":false,"usgs":false,"family":"Poulton","given":"Simon","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":711755,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Bekker, Andrey","contributorId":196849,"corporation":false,"usgs":false,"family":"Bekker","given":"Andrey","email":"","affiliations":[],"preferred":false,"id":711756,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Lyons, Timothy W.","contributorId":196850,"corporation":false,"usgs":false,"family":"Lyons","given":"Timothy","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":711757,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Konhauser, Kurt O.","contributorId":196851,"corporation":false,"usgs":false,"family":"Konhauser","given":"Kurt","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":711758,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70191103,"text":"70191103 - 2016 - Survival and metamorphosis of larval sea lamprey (Petromyzon marinus) residing in Lakes Michigan and Huron near river mouths","interactions":[],"lastModifiedDate":"2018-03-28T11:21:45","indexId":"70191103","displayToPublicDate":"2016-12-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Survival and metamorphosis of larval sea lamprey (Petromyzon marinus) residing in Lakes Michigan and Huron near river mouths","title":"Survival and metamorphosis of larval sea lamprey (Petromyzon marinus) residing in Lakes Michigan and Huron near river mouths","docAbstract":"Although population demographics of larval lampreys in streams have been studied extensively, demographics in lake environments have not. Here, we estimated survival and rates of metamorphosis for larval sea lamprey (Petromyzon marinus) populations residing in the Great Lakes near river mouths (hereafter termed lentic areas). Tagged larvae were stocked and a Bayesian multi-state tag-recovery model was used to investigate population parameters associated with tag recovery, including survival and metamorphosis probabilities. Compared to previous studies of larvae in streams, larval growth in lentic areas was substantially slower (Brody growth coefficient = 0.00132; estimate based on the recovery of six tagged larvae), survival was slightly greater (annual survival = 63%), and the length at which 50% of the larvae would be expected to metamorphose was substantially shorter (126 mm). Stochastic simulations were used to estimate the production of parasitic stage (juvenile) sea lamprey from a hypothetical population of larvae in a lentic environment. Production of juvenile sea lamprey was substantial because, even though larval growth in these environments was slow relative to stream environments, survival was high and length at metamorphosis was less. However, estimated production of juvenile sea lamprey was less for the lentic environment than for similar simulations for river environments where larvae grew faster. In circumstances where the cost to kill a larva with lampricide was equal and control funds are limited, sea lamprey control effort may be best directed toward larvae in streams with fast-growing larvae, because stream-produced larvae will most likely contribute to juvenile sea lamprey populations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2016.09.003","usgsCitation":"Johnson, N.S., Brenden, T.O., Swink, W.D., and Lipps, M.A., 2016, Survival and metamorphosis of larval sea lamprey (Petromyzon marinus) residing in Lakes Michigan and Huron near river mouths: Journal of Great Lakes Research, v. 42, no. 6, p. 1461-1469, https://doi.org/10.1016/j.jglr.2016.09.003.","productDescription":"9 p.","startPage":"1461","endPage":"1469","ipdsId":"IP-079098","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":346104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Huron, Lake Michigan","volume":"42","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59cb6731e4b017cf3141c690","contributors":{"authors":[{"text":"Johnson, Nicholas S. njohnson@usgs.gov","contributorId":145440,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas","email":"njohnson@usgs.gov","middleInitial":"S.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":711228,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brenden, Travis O.","contributorId":126759,"corporation":false,"usgs":false,"family":"Brenden","given":"Travis","email":"","middleInitial":"O.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":711229,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swink, William D.","contributorId":126758,"corporation":false,"usgs":false,"family":"Swink","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":6595,"text":"Retired USGS Fishery Biologist","active":true,"usgs":false}],"preferred":false,"id":711230,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lipps, Mathew A.","contributorId":196713,"corporation":false,"usgs":false,"family":"Lipps","given":"Mathew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":711231,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}